In a speech given just a few weeks before he was lost at sea off the California coast in January 2007, Jim Gray, a database software pioneer and a Microsoft researcher, sketched out an argument that computing was fundamentally transforming the practice of science.
Dr. Gray called the shift a “fourth paradigm.”
The first three paradigms were experimental, theoretical and, more recently, computational science.
He explained this paradigm as an evolving era in which an “exaflood” of observational data was threatening to overwhelm scientists. The only way to cope with it, he argued, was a new generation of scientific computing tools to manage, visualize and analyze the data flood.
In computing circles, Dr. Gray’s crusade was described as, “It’s the data, stupid.” It was a point of view that caused him to break ranks with the supercomputing nobility, who for decades focused on building machines that calculated at picosecond intervals.
He argued that government should instead focus on supporting cheaper clusters of computers to manage and process all this data. This is distributed computing, in which a nation full of personal computers can crunch the pools of data involved in the search for extraterrestrial intelligence, or protein folding.
Now, as a testimony to his passion and vision, colleagues at Microsoft Research, the company’s laboratory that is focused on science and computer science, have published a tribute to Dr. Gray’s perspective in “The Fourth Paradigm: Data-Intensive Scientific Discovery.” It is a collection of essays written by Microsoft’s scientists and outside scientists, some of whose research is being financed by the software publisher.
The essays focus on research on the earth and environment, health and well-being, scientific infrastructure and the way in which computers and networks are transforming scholarly communication. The essays also chronicle a new generation of scientific instruments that are increasingly part sensor, part computer, and which are capable of producing and capturing vast floods of data.
From The New York Times
View Full Article